The MEK5-ERK5 kinase axis controls lipid metabolism in small … · Author Manuscript Published...

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The MEK5-ERK5 kinase axis controls lipid metabolism in small cell lung cancer

Sandra Cristea1,2, Garry L. Coles1,2, Daniel Hornburg2, Maya Gershkovitz1,2, Julia Arand1,2, Siqi

Cao1,2, Triparna Sen3&, Stuart C. Williamson1,2,4, Jun W. Kim1,2, Alexandros P. Drainas1,2,

Andrew He1,2, Laurent Le Cam5, Lauren A. Byers3, Michael P. Snyder2, Kévin Contrepois2,

Julien Sage1,2*

1Department of Pediatrics, 2Department of Genetics, Stanford University, Stanford, CA 94305,

USA. 3Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD

Anderson Cancer Center, Houston, TX 77030, USA. 4Clinical and Experimental Pharmacology

Group, Cancer Research UK Manchester Institute, Manchester M20 4BX, UK. 5IRCM, Institut de

Recherche en Cancérologie de Montpellier, INSERM, Université de Montpellier, Institut

Régional du Cancer de Montpellier, 34298 Montpellier, France.

&Present address: Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Running title: Control of Lipid Metabolism by MEK5 and ERK5 in SCLC

To whom correspondence should be addressed:

Julien Sage, Stanford University School of Medicine, Mail code: 5149; 265 Campus Drive,

SIM1, Stanford, CA 94305. Phone: (650) 724-9246. E-mail: [email protected].

Financial Support: This work was supported by the Department of Defense (grant W81XWH-

15-1-0250 to J.S.), the National Institute of Health (grants R01CA206540, R01CA201513,

U01CA213273, and R35CA231997 to J.S., grant F31CA206346 to S.C., grant CA16672 to MD

Anderson RPPA facility, grant 3P50HG00773505S1 to M.P.S.), the American Cancer Society

(ACS) postdoctoral fellowship (to G.L.C.), the National Science Foundation Graduate Research

Fellowship (to S.C.), a CRUK-Fulbright scholarship (to S.C.W.), the Emerson Collective (to

on March 5, 2020. © 2020 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 22, 2020; DOI: 10.1158/0008-5472.CAN-19-1027

mailto:[email protected]

http://cancerres.aacrjournals.org/

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J.S.), the Lung Cancer Research Foundation (LCRF) (T.S.), a CRUK-Fulbright scholarship

(S.C.W.), and the UICC (Union for International Cancer Control) Yamagiwa Yoshida Memorial

International study fund and Cancéropôle Grand Sud Ouest (to L.L.C.). J.S. is the Harriet and

Mary Zelencik Scientist in Children’s Cancer and Blood Diseases.

Disclosure of Potential Conflicts of Interests: J.S. receives research funding from

Stemcentrx/Abbvie and Pfizer, and owns stock from Forty Seven Inc. M.P.S. is a founder and

member of the science advisory board of Personalis, SensOmics, and Qbio and a science

advisory board member of Genapsys.




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ABSTRACT

Small cell lung cancer (SCLC) is an aggressive form of lung cancer with dismal survival rates.

While kinases often play key roles driving tumorigenesis, there are strikingly few kinases known

to promote the development of SCLC. Here we investigated the contribution of the MAP kinase

module MEK5/ERK5 to SCLC growth. MEK5 and ERK5 were required for optimal survival and

expansion of SCLC cell lines in vitro and in vivo. Transcriptomics analyses identified a role for

the MEK5-ERK5 axis in the metabolism of SCLC cells, including lipid metabolism. In-depth

lipidomics analyses showed that loss of MEK5/ERK5 perturbs several lipid metabolism

pathways, including the mevalonate pathway that controls cholesterol synthesis. Notably,

depletion of MEK5/ERK5 sensitized SCLC cells to pharmacological inhibition of the mevalonate

pathway by statins. These data identify a new MEK5-ERK5-lipid metabolism axis that promotes

the growth of SCLC.

Significance

This study is the first to investigate MEK5 and ERK5 in SCLC, linking the activity of these two

kinases to the control of cell survival and lipid metabolism.




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INTRODUCTION

Small cell lung cancer (SCLC) is a subtype of lung cancer characterized by features of

neuroendocrine differentiation, rapid growth, and a high metastatic potential. More than 200,000

patients die from SCLC every year worldwide. As smoking rates increase in several parts of the

world, the number of patients developing and succumbing to SCLC continues to grow. SCLC

patients are usually treated with a combination of radiation therapy and chemotherapy.

However, resistant tumors usually emerge within months; at this point, therapeutic options are

very limited, leading to the dismal survival rates of this disease (reviewed in (1,2)). Recent

observations indicate that immunotherapies may help treat subsets of SCLC patients (3).

Similarly, targeting DNA repair pathways may prove useful to induce cell death in SCLC cells

and inhibit the growth of SCLC tumors (4). Nonetheless, it is critical to identify and investigate

additional therapeutic options, requiring a deeper understanding of SCLC biology, and the

pathways underlying its tumorigenicity.

Resection of SCLC is rare, which, for many years, has limited the number of samples

available for analysis. More recently, however, a global effort among multiple groups resulted in

a more substantial collection of SCLC samples, and an investigation of the genetic and genomic

events that may drive the growth of SCLC (5-7). A notable genetic feature of SCLC is that the

recurrent mutations observed are often loss-of-function events that inactivate tumor

suppressors, including nearly ubiquitous inactivation of the RB1 and TP53 tumor suppressor

genes. A few oncogenic drivers have been identified, including transcription factors such as

MYC family members and NFIB. Some of these gain- and loss-of-function events have been

validated as drivers of SCLC growth in genetically engineered mouse models and human cells

and may represent new therapeutic opportunities, including c-Myc (8) or CREBBP (9). However,

the striking rarity of reoccurring oncogenic driving mutations points to the existence of

unexplored key vulnerabilities in SCLC (5-7).




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The dysregulation of kinase signaling is an essential driver of oncogenic growth in multiple

contexts (10). SCLC tumors have very few activating events in genes coding for kinases

(reviewed in (11)). Nevertheless, work on kinases implicated in the response to DNA damage,

including WEE1 and CHK1 (12-14), shows that such kinases are promising targets in this

disease. There is little evidence for a role for canonical MAPK signaling (MEK1-ERK1/2) in

SCLC (11), but the less-studied MEK5-ERK5 kinase axis has not yet been investigated in SCLC

oncogenesis. In other cancers, the MEK5-ERK5 axis has been observed to play roles in many

different pathways, with multiple phenotypic results, and these two kinases have emerged as

possible therapeutic targets (reviewed in (15-17)). This dual kinase axis is responsible for

increased growth or metastasis, lower overall survival, or resistance to therapies in multiple

tumor types, including breast cancer (16,18-20), prostate cancer (21), colon cancer (18),

hepatocellular carcinomas (18,21), and high-grade osteosarcomas (18). Overall, however, the

molecular mechanisms and intracellular consequences of MEK5 and ERK5 actions leading to

these cancer phenotypes are not well understood. Here we sought to investigate the role of

these two kinases in SCLC. We found that MEK5 and ERK5 play a critical role for the survival of

SCLC cells. We also determined that MEK5 and ERK5 control lipid metabolism in SCLC cells,

including cholesterol metabolism, suggesting possible future therapeutic avenues for SCLC

treatment.




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METHODS

Ethics statement

Mice were maintained according to practices prescribed by the NIH at Stanford’s Research

Animal Facility accredited by the American Association for Accreditation of Laboratory Animal

Care (AAALAC). All animal studies were conducted following approval from the Stanford Animal

Care and Use Committee (IACUC).

In Vivo Growth Assays

Cells to be injected were stained for viability with Trypan Blue solution (Sigma-Aldrich cat #

T8154) and counted using a Countess II FL Automated Cell Counter. 1 million cells were

injected subcutaneously per flank of each NSG mouse, in 100 µL RPMI media without any

serum or antibiotics, and 100 µL Corning Matrigel Matrix (Phenol-Red-Free). Tumors were then

monitored for growth, and mice were sacrificed at 21 days post-injection. Tumors were

measured by caliper, and tumor volume was calculated using the formula

(4π/3)((length+width)/4))3. Each cell line was injected into both flanks for 2 different mice. When

graphing, the volumes of tumors on different flanks of the same mouse were averaged.

Cell culture

SCLC cell lines were maintained and passaged as described before (22). All cell lines were

passaged and grown in RPMI-1640 media supplemented with 10% bovine growth serum (BGS)

(Fisher Scientific) (unless stated as 2% serum), and penicillin-streptomycin-glutamine (Gibco).

These cells grow as suspension spheres or aggregates in culture. All cell lines were maintained

at 37°C in a humidified chamber with 5% CO2. KP1 murine SCLC cells and NJH29 human

SCLC cells have previously been described (5). All cell lines were routinely testing for

mycoplasma (MycoAlertTM Mycoplasma detection Kit, Lonza, LT07-418) and all the cells used in

this study were negative. Mouse cell lines were derived from mouse tumors and genotyped for




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genetic loss of Rb and p53. Human cell lines were purchased from ATCC (except for NJH29,

which was developed at Stanford University from a SCLC patient) and not further authenticated.

Proliferation assays, cell cycle and cell death assays, gene knock-down and gene

expression were performed largely as before (22). See Supplementary Methods file for details.

Atorvastatin Treatment and IC50 Assays

Atorvastatin Calcium (Selleckchem, #S2077) was dissolved in DMSO as per manufacturer’s

instructions. 2 x 104 cells of each cell line treated were plated per well of a 96-well plate, in 90

µL of reduced (2% BGS) serum media, in triplicate for each treatment condition. Cells were

allowed to re-form their spheroids (in the case of mSCLC KP1 cells) or clumps (in the case of

many hSCLC cells) or to adhere (in the case of some hSCLC cell lines, such as SBC5) for 24

hours, after which a 10x concentration of atorvastatin or vehicle control (DMOS) in 2% BGS

media was added to each well, in a 10 µL volume for a total volume of 100µL per well. 48 hours

or 5 days later, plates were read using the alamarBlue reagent (ThermoScientific, DAL1100), as

described above. Atorvastatin was used at final concentrations of 0 (DMSO only), 2, 5, 10 and

20 µM. The fraction of remaining viable cells was calculated by dividing the averaged

fluorescence signal of each concentration replicate set for each cell line, by the vehicle control

averaged values for that same cell line.

For atorvastatin IC50 assays on MEK5/ERK5-knockdown cells, hSCLC NJH29 cells were

plated similarly, at 2 x 104 cells per well of a 96-well plate, for each shRNA-expressing sample,

in 90 µL of reduced (2% BGS) serum media, in triplicate for each treatment condition. Plates

were read using alamarBlue after 48 hours of atorvastatin treatment, and. IC50 values were

calculated using GraphPad Prism 7.

Immunoassays

Protein levels were determined by immunoblot or using the Simple WesternTM quantitative




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immunoassay and the Compass software, according to the manufacturer’s protocol. Cells were

lysed in RIPA lysis buffer from ThermoFisher Scientific (cat # 89900), supplemented with

proteasome and phosphatase inhibitors, and lysates were cleared by centrifugation at maximum

speed for 10 minutes, and sonicated for 30 seconds each. Total protein was quantified using the

Pierce BCA Protein Assay Kit (Thermo Fisher, Cat. #23277). For Simple WesternTM, whole-cell

lysates were diluted to a final concentration of 0.2 µL/mL. For Simple WesternTM immunoassays

the antibodies and dilutions used were as follows: ERK5 (D23E9) rabbit mAb (Cell Signaling

Technology, CST #3552, 1:100), ERK5 (D3I5V) rabbit mAb (CST, #12950, 1:50) phospho-

ERK5 (Thr218/Tyr220) rabbit polyclonal antibody (CST #3371, 1:50), PCNA (PC10) mouse

mAb (CST #2586, 1:100), cleaved PARP (D124) rabbit mAb (CST #9664, 1:100), Tubulin

mouse mAb, (Sigma-Aldrich T9026, 1:500), HSP90 (C45G5) rabbit mAb (CST #4877,

1:10,000), NEUROD1 (D35G2) rabbit mAb (CST #4373, 1:2000), and MASH1/ASCL1 mouse

mAb (BD Biosciences, # 556604, 1:1000). All other conditions and reagents were as suggested

by the manufacturer. A representative example of quantification with raw data is shown in

Supplementary Table S16. For immunoblot, washing was done in Tris-buffered saline washing

buffer with 0.1% Tween-20, blocking was done with 10% milk in washing buffer, and antibodies

were diluted in 5% milk in washing buffer. Antibodies used in immunoassays were as follows:

MEK5 mouse mAb antibody (sc-135986, 1:1000), HMGCR rabbit mAb (Abcam, ab174830,

1:500), CC3 (D175) rabbit polyclonal antibody (CST #9541, 1:100) HSP90 rabbit mAb (CST

#4877, 1:10,000), and peroxidase-AffiniPure goat anti-Rabbit IgG antibody (Jackson Immuno-

Research, # 111-035-144, 1:10,000).

Reverse-Phase Protein Array

Human SCLC NJH29 cells were infected with lentiviral constructs expressing an shRNA

against MEK5 and two independent shRNAs against ERK5, as well as two control shRNAs

(shGFP and shSCR). These samples were infected, selected, expanded, and grown in reduced




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serum media (2% BGS) for 3-4 days in independent triplicates, Lysates were prepared as

required by the MD Anderson RPPA facility as previously reported (23), and submitted to the

facility, where RPPA processing and statistical analysis was performed as before (23). Fold

change values are shown unlogged, and p-values are from simple student’s t test calculations,

with all replicates of shRNAs against MEK5 in one group, against ERK5 in another group, and

against “controls” (shGFP and shSCR) in an “shCTRL” group.

RNA-seq library preparation, RNA-seq analysis, and Gene Set Enrichment Analysis

mSCLC KP1 cells and hSCLC NJH29 cells were grown in RPMI-1640 media supplemented

with 10% bovine growth serum (BGS) (Fisher Scientific) and penicillin-streptomycin-glutamine

(Gibco). After infection with respective lentiviral shRNA constructs in triplicate, the cells were

subjected to 4 days of puromycin selection (ThermoFisher cat # A1113803, 2 µg/mL for mSCLC

cells and 2.5 µg/mL for hSCLC cells), then allowed to recover for 1-2 days without puromycin,

and expanded for 3-4 days in 2% serum RPMI media. The details of RNA extraction, library

preparation, sequencing and analysis can be found in the Supplementary Methods file.

Lipid staining and Lipidomics

Lipid staining was performed with BODIPY™ 493/503 dye (4,4-Difluoro-1,3,5,7,8-Pentamethyl-

4-Bora-3a,4a-Diaza-s-Indacene, ThermoFisher, #D3922) according to the manufacturer’s

instructions. Details can be found in the Supplementary Methods file.

Sample Preparation. mSCLC KP1 cells were infected with two hairpins per gene (MEK5 and

ERK5), as well as two control hairpins (shGFP and shSCR), in completely independent

triplicates. These samples were subjected to selection and recovery, and allowed to expand in

2% BGS media. Cells were trypsinized and counted, and 107 live cells per sample were washed

three times with 10 mL phosphate-buffered saline (PBS), and snap-frozen in Eppendorf Safe-

Lock 2 mL tubes (Eppendorf, # 022363344) by dropping in liquid nitrogen, and stored at -80ºC.




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Cell pellets were then stored at -80ºC until further processing. The details of lipid extraction,

measurements, and analysis can be found in the Supplementary Methods file.

DepMap Analysis

Dependency scores for all assayed genes in all 25 hSCLC cell lines were extracted from the

complete Combined RNAi (Broad, Novartis, Marcotte) dataset available on the Cancer

Dependency Map website, downloaded from the Data tab (depmap.org/portal/download/) in

August of 2018. Pearson correlations between the dependency scores of MEK5 and ERK5,

respectively, and those of all other genes across the hSCLC cell lines, were calculated using the

corrplot R package. These sets of genes were then compared to find genes with dependency

scores that correlated “highly” (r>0.5) with both MEK5 and ERK5 dependencies in hSCLC cell

lines. The resulting 63 genes were then analysed with Enrichr (amp.pharm.mssm.edu/Enrichr/)

to find GO Molecular Functions, GO Biological Processes, KEGG Pathways, and WikiPathways

lists, as well as statistical significance for each term.

REVIGO Analysis

MEK5 and ERK5 dependency-correlated genes were analysed by Enrichr as stated above; GO

Biological Processes terms associated with the 63 genes overlapping for MEK5 and ERK5

(Table S10) were then analysed and visualized using REVIGO (revigo.irb.hr/). GO ID numbers

and adjusted p values were entered into the REVIGO field, and analysis was performed with

“Medium(0.7)” allowed similarity, the Homo sapiens GO database, and the SimRel (default)

semantic similarity measure. The resulting scatterplot is shown with slight aesthetic changes,

and only GO terms with dispensability scores < 0.05 are labelled.

Statistics

Statistical significance was assayed with the GraphPad Prism 7 software. *: p-value<0.05; **: p-

value<0.01; ***: p-value<0.001; ****: p-value<0.0001; ns: not significant. The tests used are



https://depmap.org/portal/download/

http://amp.pharm.mssm.edu/Enrichr/

http://revigo.irb.hr/


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indicated in the figure legends. To compare growth curves, we used the 2-way ANOVA followed

by t-tests. When comparing more than 2 groups, we first performed one-way ANOVA, followed

by t-tests. If F-test for variance showed a significantly different distribution between two groups

being compared (F-test p<0.05), the nonparametric Mann-Whitney p-value is reported instead

of the student’s t-test p-value, with significance symbols as described above. Data are

represented as mean+/-SD unless otherwise stated. To calculate the significance of the overlap

between two groups of genes, the hypergeometric test was used

(systems.crump.ucla.edu/hypergeometric/index.php), with the “population size” being the sum of

all genes identified after filtering, regardless of p-value “number of successes in population”

being the size of one list being considered (list 1), “sample size” being the size of the second list

being overlapped (list 2), and “number of successes” being the overlap between list 1 and list 2.



http://systems.crump.ucla.edu/hypergeometric/index.php


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RESULTS

Depletion of MEK5 or ERK5 inhibits the expansion of SCLC cell populations

Surveying the data from the RNAi Cancer Dependency Map (24), we found that the majority

of the 25 human SCLC cell lines tested show some dependency on MAP2K5, the gene coding

for MEK5 (Supplementary Fig. S1A). The Cancer Dependency Map analysis in SCLC cell lines

for MAPK7, coding for ERK5, showed little to no dependency (see depmap.org), suggesting that

MEK5 may have additional targets in SCLC cells or that ERK5 loss is easier to compensate for

in SCLC cells. Both MAP2K5 and MAPK7 are expressed at intermediate levels in human SCLC

tumors (Supplementary Table S1). Data from the cBioPortal show occasional genetic events

implicating the MAP2K5 and MAPK7 genes, with no reported recurrent events in SCLC but a

missense mutation in the MAPK7 gene (A501D) in 2/88 patients with adrenocortical carcinoma,

suggestive of a possible oncogenic role for this kinase in neuroendocrine cancers (see

cbioportal.org).

These observations and the absence of published data on MEK5-ERK5 in SCLC prompted

us to further investigate the role of MEK5 and ERK5 in this form of lung cancer. To this end, we

first knocked down these two kinases in mouse and human SCLC cells with independent sets of

shRNA molecules (Supplementary Fig. S1B-C). Upon knock-down of the MEK5-ERK5 axis,

human SCLC (hSCLC) NJH29 cells and murine SCLC (mSCLC) KP1 cell populations grew

slower compared to cells expressing control shRNAs (Fig. 1A-B and Supplementary Fig. S1D).

As expected, phosphorylated ERK5 was downregulated when MEK5 was knocked down

(Supplementary Fig. S1E). We also performed subcutaneous tumor growth assays in

immunocompromised NOD-Scid-Gamma (NSG) mice and found that injection of MEK5- or

ERK5-depleted cells resulted in lower tumor volumes compared to control knock-down cells

(Fig. 1C-D). There was no evidence of counter-selection for the MEK5 knock-down in the

context of these experiments (Supplementary Fig. S1F). Thus, the MEK5 and ERK5 kinases




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contribute to the optimal expansion of SCLC cell populations in culture and in vivo.

Depletion of MEK5 or ERK5 induces cell death in SCLC cell populations

The long-term growth of SCLC cells is driven by lineage transcription factors such as ASCL1

or NEUROD1 implicated in neuroendocrine identity (reviewed in (25)). We investigated whether

MEK5 and ERK5 regulate the levels of these proteins. However, protein levels of ASCL1 were

not affected by the loss of MEK5 in the ASCL1-high KP1 cells, and levels of NEUROD1

increased upon MEK5 loss in NEUROD1-high NJH29 cells (Supplementary Fig. S2A-B). These

results suggested that the inhibition of SCLC growth upon reduction of the MEK5-ERK5 axis

was not directly connected to these transcription factors and neuroendocrine cell identity.

ERK5 is a kinase that can also function as a transcription activator (26,27). To determine the

function(s) of ERK5 that are important for the expansion of SCLC cells, we re-introduced into

ERK5 knockdown NJH29 human SCLC cells either wild-type ERK5 or mutant forms of ERK5

impaired for its transcriptional or kinase activities (Figure 1E-F). The inhibition of growth

observed upon ERK5 knockdown was rescued by wild-type ERK5 as well as by a truncation

mutant that abolishes the transcriptional activity; in contrast, two separate kinase-dead mutant

failed to rescue the growth defects (Figure 1G), indicating that ERK5 kinase activity is important

for the optimal growth of SCLC cell populations in this context.

Based on these observations, to further investigate the role of the MEK5-ERK5 axis in

SCLC, we next queried a number of signaling pathways, many mediated by phosphocascades,

using a Reverse-Phase Protein Array (RPPA) approach (28) in MEK5 and ERK5 knock-down

cells. Following this antibody‐based functional proteomic analysis, unsupervised clustering

grouped MEK5 and ERK5 knock-down cells together, while control cells had distinct profiles,

indicating that MEK5 and ERK5 belong to the same phosphocascade in SCLC cells (Fig. 2A,

Supplementary Fig. S2C-D, and Supplementary Table S2). However, few major concerted




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changes were identified in specific signaling pathways in MEK5 and ERK5 knock-down cells

compared to controls. One exception was changes in proteins implicated in apoptotic cell death,

such as increased detection of cleaved caspase 7, cleaved PARP, and the pro-apoptotic factor

BIM, upon MEK5-ERK5 depletion. Consistent with these findings, MEK5 and ERK5 knock-down

cells showed a higher propensity toward apoptotic cell death in culture (Fig. 2B-C). No

significant changes were observed in cell cycle profiles under the same conditions in culture

(Supplementary Fig. S2E-F). When we examined markers of cell cycle progression and cell

death in vivo in xenografts from NJH29 cells upon ERK5 knock-down, we found no obvious

changes in PCNA, a marker of DNA replication, and a trend towards increased cleaved PARP

and cleaved Caspase 3, two markers of apoptotic cell death (Fig. 2D-F and Fig. S2G).

Together, these observations indicate that the MEK5-ERK5 axis controls the expansion of

SCLC cells mainly by promoting their survival.

Depletion of MEK5 or ERK5 perturbs gene programs associated with metabolic pathways

To gain further insights into the mechanisms by which the MEK5 and ERK5 proteins

promote the survival and the expansion of SCLC cell populations, we performed transcriptional

RNA-seq analyses of SCLC cells with an impaired MEK5-ERK5 axis (Supplementary Fig. S3A

and Supplementary Tables S3-4). In unsupervised hierarchical clustering analyses (Fig. 3A) and

principal component analysis (PCA) of the gene expression data (Supplementary Fig. S3B),

mSCLC KP1 cells with MEK5 and ERK5 knock-down mSCLC KP1 cells clustered separately

from controls; similarly, MEK5 knock-down hSCLC NJH29 cells clustered separately from

control cells (Fig. 3A and Supplementary Fig. S3A-B). ERK5 knock-down NJH29 cells were not

investigated using this assay. A significant overlap was found between the genes

downregulated upon MEK5 knock-down in KP1 and NJH29 cells, suggesting that MEK5 has a

similar role in promoting gene expression programs in these two contexts

(Supplementary Fig. S3C). Additionally, the programs affected by MEK5 and ERK5 knock-down




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overlapped significantly in KP1 cells, confirming that the two kinases, indeed, function in the

same phosphocascade (Fig. 3B). GSEA (Gene Set Enrichment Analysis) of averaged log2 fold

change values in murine MEK5 and ERK5 knock-down SCLC cells pointed at the

downregulation of metabolic pathways upon reduction in MEK5 and ERK5 levels (Fig. 3C and

Supplementary Table S5). Similar metabolic pathways were found downregulated at the

transcriptional levels in NJH29 cells upon MEK5 knock-down (Supplementary Figure S3D and

Supplementary Table S6). Neither mSCLC nor hSCLC cells with MEK5-ERK5 axis knock-down

showed an enrichment in cell cycle or neuroendocrine genes (Supplementary Figure S4A-B),

further supporting that the loss of viability of SCLC cells after MEK5-ERK5 depletion is not due

to a change in neuroendocrine status or their proliferation rate.

We also investigated the genes whose dependency scores were most correlated with those

for MEK5 or ERK5 in the 25 human SCLC cell lines of the Cancer Dependency Map project.

298 and 293 genes had a dependency score Pearson correlation coefficient greater than 0.5 for

MEK5 and ERK5, respectively, with an overlap of 63 genes (Supplementary Table S7). Enrichr

analysis of these 63 genes confirmed a link between MEK5 and ERK5 and metabolic pathways,

with the most significant GO Molecular Functions terms suggested links to malate metabolism

and phosphofructokinase activity (Supplementary Table S8); GO Biological Process analysis

further highlighted malate metabolism, NADH metabolism, oxaloacetate metabolism, and

Vitamin D biosynthesis (Supplementary Table S9 and summarized by REVIGO analysis in

Fig. 3D and Supplementary Table S10). KEGG pathway analysis and WikiPathways highlighted

connections between MEK5-ERK5 signaling and pyruvate metabolism and glyoxylate and

dicarboxylate metabolism, as well as the citrate cycle and glycolysis

(Supplementary Tables S11-S12).

Together, this analysis of transcriptional networks and dependency links loss of MEK5 or

ERK5 in SCLC cells to perturbations in metabolism. In particular, a number of genes and




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pathways found to be altered in the RNA-seq and Cancer Dependency Map analyses of MEK5-

ERK5 deficient SCLC cells pointed to lipid metabolism-related pathways, including those

implicated in cholesterol homeostasis and de novo fatty acid (FA) synthesis. These data

suggest that altered metabolism, including lipid metabolism, may contribute to decreased

survival and growth inhibition upon inactivation of the MEK5-ERK5 module.

MEK5 and ERK5 knockdown affects cholesterol synthesis pathways

Little to nothing is known about lipid metabolism in SCLC and the links between the MEK5-

ERK5 axis and lipid homeostasis (29,30). Consistent with our findings, one of the most

significant transcription factor target gene sets to be identified by GSEA as disenriched in MEK5

and ERK5-deficient cells, was SREBP (Sterol-Regulatory Element Binding Protein) target genes

(Supplementary Figure S5A and Supplementary Table S13). Indeed, SREBP targets (31) were

downregulated in MEK5-deficient human and murine cells and ERK5-deficient murine cells

(Fig. 4A and Supplementary Figure S5B). Briefly, the SREBP pathway has two arms, including

the mevalonate pathway regulated by SREBF2, which results in cholesterol synthesis, and the

fatty acid synthesis pathway regulated by SREBF1 (32). Staining of MEK5 and ERK5 knock-

down cells with BODIPY showed no significant decrease in total neutral lipid content compared

to controls (Supplementary Fig. S5C). To more specifically ascertain which components of lipid

synthesis were dysregulated following MEK5 and ERK5 knock-down, we subjected murine

SCLC cells (KP1) to targeted lipidomics analyses (Fig. 4B, Supplementary Fig. S5D,

Supplementary Tables S14-S15). Unsupervised hierarchical clustering and PCA clustered

MEK5 and ERK5 knock-down SCLC cells separately from controls

(Supplementary Figure S6A). Additionally, though multiple lipid species were changing

significantly, the only lipid classes to show all significantly changing species as reduced in

abundance in both MEK5 and ERK5 knock-down cells were cholesteryl esters (CE),

diacylglycerols (DAG) and dihydroceramides (DCER) (Fig. 4B and Supplementary Figure S6B).




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Out of the classes with relative decreased abundance as a consequence of MEK5-ERK5

knockdown, the DCER lipid class was represented by a single specific DCER significantly

downregulated in both MEK5 and ERK5 knock-down samples (Supplementary Figure S6B).

Among the remaining two classes, decreased CE abundance was predominant in MEK5 knock-

down cells, while DAG was the lipid class predominantly downregulated in ERK5 knock-down

cells. Free fatty acids (FFAs) were not significantly altered in MEK5-ERK5 cells; in addition,

although the relative abundance of a diverse range of triacylglycerol (TAG) species, into which

FAs are incorporated for energy storage, changed, they did so in both directions (Fig. 4B and

Supplementary Tables S14-S15). In contrast, the relative abundance of all detected CEs was

significantly decreased in MEK5-knockdown mSCLC KP1 cells, and several were also reduced

in ERK5-knockdown cells (Supplementary Fig. S6C – see also Methods and

Supplementary Fig. S6D). Together, these data point to cholesterol biosynthesis pathways

downstream of MEK5-ERK5 and suggest that these metabolic defects could contribute to the

loss of viability of SCLC cells upon MEK5 or ERK5 depletion.

Inhibition of the MEK5-ERK5 axis and the mevalonate pathway can both limit the

expansion of SCLC cells

The mevalonate pathway is composed of a sequence of enzymatic steps that convert

Acetyl-CoEnzymeA into cholesterol and isoprenoids (33). Consistent with our lipidomic

analyses, genes encoding enzymes involved in different steps of this pathway were

downregulated by shMEK5 in human and murine SCLC cells, and by shERK5 in murine SCLC

cells (Fig. 5A and Supplementary Fig. S7A). One of the most clinically-relevant inhibitors of this

pathway is atorvastatin calcium (known commercially as Lipitor) (34), an inhibitor of the rate-

limiting enzyme HMG-CoA-Reductase (HMGCR). HMGCR levels were lower in MEK5 and

ERK5 knock-down cells at the RNA level (Fig. 5A). HMGCR protein levels were also lower in

ERK5 knock-down cells (Fig. 5B). To evaluate the importance of the mevalonate pathway in




18

SCLC, we first treated 8 cell lines (7 hSCLC and 1 mSCLC cell line (KP1)) with low micromolar

doses of atorvastatin for 2 or 5 days. Atorvastatin decreased viability by an average of 50% cells

across the human SCLC cell lines and ~60% in mouse KP1 cells alive after 2 days of treatment,

with a decreased viability of ~80% by day 5 (Fig. 5C). These results are in line with the inhibitory

activity of two related compounds, simvastatin and fluvastatin on hSCLC cell lines in a recent

large-scale screening effort (35) (Supplementary Fig. S7B).

These data identifying a previously unknown connection between the MEK5-ERK5 axis and

the mevalonate pathway in SCLC cells led us to test the possibility that inhibiting both pathways

simultaneously may have a greater effect on the expansion of SCLC cells than the inhibition of

each single pathway. We focused on the NJH29 cell line - one of the most resistant to

atorvastatin treatment in our study. Strikingly, ERK5 depletion in NJH29 cells sensitized these

cells to further inhibition of the mevalonate pathway by low doses of atorvastatin (Fig. 5D and

(Supplementary Fig. S7C). These experiments further the functional link between the MEK5-

ERK5 axis and the mevalonate pathway in SCLC.




19

DISCUSSION

Small cell lung cancer (SCLC) is a disease with a dire survival rate reflective of late

diagnosis, incredibly quick metastasis, few and ineffective treatment choices, and an underlying

lack of knowledge about its basic biology. Here we focused on the relatively unexplored MEK5-

ERK5 axis, a pathway that is not recurrently altered at the genetic level but that we

hypothesized could be a driving force in SCLC. Accumulating evidence supports an important

role for MEK5 and ERK5 in various phenotypes associated with cancer, even though the pro-

tumorigenic effects of these two enzymes have not been associated with genetic events leading

to their activation (15-17). We identified a pro-survival role for these kinases in SCLC cells and

show that they are implicated in the control of cholesterol synthesis and other lipid metabolism

pathways in SCLC cells.

We identified a pro-survival role for these kinases in SCLC cells. A similar role in the control

of survival has been described for MEK5 and ERK5 in multiple normal and cancerous cell types

(21,36,37), suggesting that this function is broadly conserved for the MEK5-ERK5 axis. The

upstream signals that activate MEK5 and ERK5 remain poorly understood, especially in the

context of pro-survival signals in cancer cells. The downstream mechanisms by which this

kinase axis promotes survival are also poorly understood but likely to be diverse. Our work and

recent work by others suggest that it may include the regulation of metabolic pathways. A recent

study identified a link between MEK5/ERK5 and the stability of MYC, a regulator of cell

metabolism and growth (38); this link is corroborated by our RNA-seq analysis (Fig. 3C).

Emerging evidence also links MEK5 and ERK5 to the control of oxidative phosphorylation (39).

Notably, one study makes a connection between forced oxidative phosphorylation and

downstream effects on cholesterol levels, via an increase in LDLR expression and intracellular

LDL-cholesterol intake – a process dependent on the MEK5-ERK5 axis (29). To our knowledge,

however, there is no prior direct description of a control of de novo cholesterol synthesis by the




20

MEK5-ERK5 axis. Further studies are required to determine if our observation is specific to

SCLC or if it applies to other cell types. Importantly, our data show that the lipid metabolism

phenotypes of MEK5 loss and ERK5 loss are very similar, but not identical, in SCLC cells, and it

is thus also possible that some of the mechanisms of survival control are different for the two

kinases. Notably, our results suggest that the kinase activity of ERK5 contributes to its pro-

tumorigenic role in SCLC, and inhibitors of both kinases in the pathway may provide therapeutic

strategies in the future, especially in combination with other therapeutic agents.

Our work also highlights the current general lack of knowledge of SCLC metabolism. SCLC,

like many tumors, is thought to be highly glycolytic. However, unlike most cancers, which

predominantly express the PKM2 isoform of pyruvate kinase M, a glycolytic enzyme that

specifies the fate of glucose-derived carbons, SCLC cells express a higher ratio of PKM1 to

PKM2 (40). This results in SCLC cells having higher glucose flux into lactate conversion and the

TCA cycle, attenuated glutamine metabolism, and likely are better at performing mitophagy, and

attaining lower ROS (reactive oxygen species) levels (41). Another recent study shows that the

ASCL1-low subtype of SCLC tumors is specifically dependent on de novo purine synthesis in

vivo (42).

While large amounts of lipids have been demonstrated to be necessary to support the rapid

proliferation of cancer cells, the implication of lipid metabolism in SCLC remains poorly

understood. A single study utilizing metabolomic profiling of one SCLC cell line (NCI-H446)

found elevated carnitine palmitoyltransferase 1A (CPT1A) and 2 (CPT2), key enzymes in fatty

acid oxidation, compared to NSCLC and normal epithelial cell controls (43). The only SCLC

study to date related to cholesterol metabolism, to our knowledge, is a correlative study that

found that low serum LDL (low-density lipoprotein), and low protein expression of LDLR (low-

density lipoprotein receptor), both independently correlate with better overall survival (44). A

previous study using simvastatin, a pharmacological inhibitor of HMCGR, the rate-limiting




21

enzyme of the mevalonate pathway, in SCLC cell lines observed a decrease in proliferation but

linked these effects to possible changes in Ras signaling and not to general cholesterol

metabolism (45). Our studies provide novel insights into lipid biology in SCLC cells and

comparisons with future analyses in other cancer types will identify unique aspects of lipid

metabolism in SCLC cells. For example, SCLC cells have been observed to require lipid-raft

mediated SRC-PI3K/AKT activation for sustained growth in culture (46). Additionally, the

isoprenoid byproducts of the mevalonate pathway are crucial for the prenylation of multiple

proteins (including RAS superfamily members), which is critical for their correct tethering,

localization, and protein-protein binding signaling functions (47). Furthermore, cholesterol itself

can be attached directly to proteins, including Smoothened (SMO) in the Hedgehog pathway, a

pathway that has been shown to control SCLC tumor initiation and progression (48,49). These

cholesterol-mediated functions regulate a wide range of cellular processes, including cell

polarity and cell body dynamics, cell proliferation and survival, protein and intracellular vesicular

trafficking, cell cycle, and nuclear transport dynamics – all of which may affect the survival and

the expansion of SCLC cells.

Targeting the mevalonate pathway as a therapeutic intervention is being investigated in

multiple tumor types (50). Our experiments exposing SCLC cell lines to atorvastatin suggest

that at least a subset of SCLC cells is sensitive to HMGCR inhibition. Interestingly, a few clinical

trials have included SCLC patients treated with statins (45,51,52), with no visible benefit for the

survival of these patients. However, given the evidence building up that HMGCR inhibitors such

as statins have multiple effects, systemically and tumor-specifically, the failure of these trials

may say more about the complexity of cholesterol inhibition in tumors and in human tissues than

it does about the degree of dependence of SCLC on cholesterol synthesis. Consistent with our

findings, a recent study identified sensitivity to inhibition of the cholesterol biosynthetic pathway

enzyme squalene epoxidase (SQLE) in SCLC cells (53). A large meta-analysis of small cell




22

neuroendocrine tumors also suggests susceptibility to disruption of lipid and sterol metabolism

(54). Additional investigation into the latter will need to be performed in order to ascertain which

of the many roles of the mevalonate pathway and its byproducts are truly critical in SCLC.

Our work suggests that the anti-cancer effects of inhibiting the mevalonate pathway may be

enhanced by inhibition of the MEK5/ERK5 axis, which may be achieved when potent and

specific inhibitors of these kinases have been developed (55). Additionally, mevalonate pathway

inhibitors were found to have vaccine-adjuvant activities and to synergize with anti-PD-1

antibodies to kill tumor cells, by enhancing the functions of antigen-presenting cells (56).

Therefore, mevalonate pathway inhibition may also have the ability to increase antigen

presentation to the immune system, perhaps serving as a future adjuvant for an SCLC vaccine

in conjunction with FDA-approved immunotherapies.

In this study, we present the first experiments suggesting that a less-studied arm of the MAP

Kinase pathway – the MEK5/ERK5 dual kinase axis – is crucial for sustained SCLC cell viability.

Furthermore, we have connected this axis to downstream cholesterol biology in SCLC,

especially the mevalonate pathway. Additional experiments need to be performed to identify the

detailed molecular mechanism of these connections, but our studies already present multiple

single and combinatorial therapeutic strategies which can be further tested and validated

preclinically, in hopes of clinical success for the hundreds of thousands of patients who die

yearly from small cell lung cancer.




23

ACKNOWLEDGMENTS

We thank the staff of the Stanford Functional Genomics Facility, the FACS Core at the Institute

for Stem Cell Biology and Regenerative Medicine, and the Stanford Veterinary Service Center,

as well as Pauline Chu, for their technical support and expertise. We acknowledge members of

the Sweet-Cordero and Sage laboratories for technical and materials support, including Andrea

Chaikovsky for her help with immunoblots. This work was supported by the Department of

Defense (grant W81XWH-15-1-0250 to J.S.), the National Institute of Health (grants

R01CA206540, R01CA201513, U01CA213273 and R35CA231997 to J.S., grant F31CA206346

to S.C., grant CA16672 to MD Anderson RPPA facility, grant P50HG007735 to M.P.S.), the

American Cancer Society (ACS) postdoctoral fellowship (to G.L.C.), the National Science

Foundation Graduate Research Fellowship (to S.C.), a CRUK-Fulbright scholarship (to S.C.W.),

the Emerson Collective (to J.S.), the Lung Cancer Research Foundation (LCRF) (T.S.), a

CRUK-Fulbright scholarship (S.C.W.), and the UICC (Union for International Cancer Control)

Yamagiwa Yoshida Memorial International study fund (to L.L.C.). J.S. is the Harriet and Mary

Zelencik Scientist in Children’s Cancer and Blood Diseases.




24

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28

FIGURE LEGENDS

Figure 1: MEK5 and ERK5 knock-down inhibits the expansion of SCLC cells

(A-B) Quantification of populations growth in reduced serum (2%) of human NJH29 SCLC cells

(A) and murine KP1 SCLC cells (B) with shRNA-mediated knockdown of MEK5 and ERK5

compared to shCTRLs by alamarBlue assay. 2-way ANOVA interaction p-values comparing the

knock-down curves to the control are shown in colors; t-test p-values shown at the right of each

graph specify comparison of each knock down controls, with ** signifying p<0.01, ***, p<0.001,

and ****, p<0.0001; n = 2 independent shRNAs per group (for hSCLC NJH29, n=5-6, and for

mSCLC KP1, n=3 independent experiments per individual hairpin).

(C-D) Volume of tumors resulting from subcutaneous injections of hSCLC NJH29 cells (C) or

mSCLC KP1 cells (D) expressing shMEK5, shERK5, or shCTRL (shGFP and shSCR), after 3

weeks of growth in the flanks of NSG recipient mice; grey and black dots represent two

independent shRNAs per group (n=4-6 independent experiments per individual hairpin, *,

p<0.05, n.s., p>0.05 by t-test following one-way ANOVA p=0.0192 for (C) and p=0.0318 for (D)).

(E) Schematic representation of the ERK5 protein with the mutants used in (F-G).

(F) Immuno-assays for ERK5 and HSP90 (loading control) in hSCLC NJH29 cells with ERK5

knockdown and re-expression of wild-type or mutant forms of ERK5, as indicated. Levels of

ERK5 relative to HSP90 are indicated below. The 116 kDa molecular weight marker is shown

on the left.

(G) Quantification of populations growth in reduced serum (2%) of human NJH29 SCLC cells as

in (B) by alamarBlue assay (n=3). 2-way ANOVA p-values comparing the kinase-dead mutants

to the wild-type ERK5 rescue are significant (p<0.0001 for both K84M and D182A); t-test p-

values shown on the right are day 6 values compared to ERK5 rescue, with * signifying p<0.05,

** signifying p<0.01 and n.s. for non significant.




29

Figure 2: MEK5 and ERK5 knock-down induces cell death in SCLC cell populations

(A) Unsupervised hierarchical clustering of all protein quantities measured by Reverse Phase

Protein Array (RPPA) separates hSCLC NJH29 cells with MEK5 and ERK5 knockdown, from

those with shCTRL knockdowns (shGFP and shSCR) (top); for proteins with average fold

change (FC) across all ERK5 and MEK5 hairpins larger than 1.15 or smaller than 0.75, protein

names and average FCs are noted.

(B-C) Knockdown of MEK5 and ERK5 in hSCLC NJH29 (B) and mSCLC KP1 (C) SCLC cells

results in a higher rate of cell death by apoptosis as measured by AnnexinV/PI staining and flow

cytometry after 2 days of growth in 2% serum conditions; n.s., p>0.05, *, p<0.05, **, p<0.01 by t-

test following one-way ANOVA (p=0.0115 for (B) and p=0.004 for (D)); grey and colored dots

represent 2 different shRNAs per group, and n=2-3 independent experiments per individual

hairpin.

(D) Volume of tumors resulting from subcutaneous injections of hSCLC NJH29 cells expressing

shERK5 (one shRNA) or shCTRL (shGFP) during 3 weeks of growth in the flanks of NSG

recipient mice (n=12 tumors per group, error bars represent S.E.M.). 2-way ANOVA interaction

p=0.0002, *** shown in graph.

(E) Immunoassays for ERK5, the cell death marker cleaved PARP (Cl PARP), and the cell cycle

marker PCNA on extracts from NJH29 tumors in (D) at day 21 of growth. Tumors were selected

to minimize differences due to tumor size (n=3 shCTRL and n=4 shERK5). Protein levels are

relative to the loading control HSP90.

(F) Immunoblot as in (E) for the cell death marker cleaved caspase 3 (CC3). Tubulin is a loading

control.




30

Figure 3: MEK5 and ERK5 knock-down perturbs metabolic pathways in SCLC cells

(A) Unsupervised clustering from RNA-seq data upon MEK5-ERK5 knockdown in mSCLC KP1

cells, and MEK5 knockdown in hSCLC NJH29 cells; allgenes with |log2FoldChange|>0.5 and

adjusted p-values <0.05 were included in the analysis from any comparison); n=2-3

independent replicates per hairpin.

(B) MEK5 and ERK5 knockdown in mSCLC KP1 cells change the transcriptome in similar ways,

with significantly overlapping gene sets being downregulated and upregulated; hypergeometric

test used to obtain p-value of overlap; only genes changing by a |log2 fold change|>0.5 and an

adjusted p-value<0.05 were considered.

(C) Hallmarks Gene Set Enrichment Analysis (GSEA) gene sets significantly enriched or

disenriched when the MEK5-ERK5 axis is downregulated in mSCLC KP1 cells compared to

controls; log2 fold change values averaged for shMEK5 and shERK5, respectively, each

compared to shCTRLs (shGFP and shSCR), to focus analysis on genes controlled by both

kinases; only enriched sets with q-values <0.3 are shown.

(D) Gene Ontology Biological Function term results from Enrichr, for the set of 63 genes with a

Pearson correlation coefficient of over 0.5 between their dependency scores and those of both

MEK5 and ERK5 in 25 hSCLC cell lines from the Cancer Dependency Map project, analyzed by

ReviGO, and mapped based on their semantic similarity; GO IDs with a dispensability score

<0.15 are numbered and stated in the legend below; bubble or plot size is proportional to

frequency of Homo sapiens UniProt entries associated with that GO ID, color specifies log10

adjusted p-value for that GO ID from Enrichr.




31

Figure 4: MEK5 and ERK5 loss changes the lipidomic profile of SCLC cells

(A) MEK5 and ERK5 knockdown cells downregulate SREBP pathway targets compared to cells

infected with shCTRLs (from RNA-seq FPM values).

(B) Significantly changing lipid species (p<0.05) between shMEK5 and shCTRL cells (left) or

between shERK5 and shCTRL cells (right) are shown in volcano plots with -log10(FDR) on the

Y axis and log2 fold change on the X axis; significantly changing lipid species (FDR<0.05) are

shown as colored dots according to their lipid classes (colors corresponding to different lipid

classes are shown in the legend at bottom); significantly changing cholesteryl esters (CE) are

labeled with their number of carbons and unsaturations contained on the fatty acid moeity; CE,

cholesteryl esters, TAG, triacylglycerols, CER, ceramides, DAG, diacylglycerols, DCER,

dihydrocermides, FFA, free fatty acids, HCER, hexosylceramides, LCER, lactosylceramindes,

SM, sphingomyelins, LPC, lysophosphatidylcholines, LPE, lysophosphatidylethanolamines, PC,

phosphatidylcholines, PE, phosphatidylethanolamines; n=2-3 independent replicates per

hairpin.




32

Figure 5: MEK5 and ERK5 knockdown results in inhibition of the mevalonate pathway

and increased sensitivity to mevalonate pathway inhibitors

(A) MEK5 and ERK5 knockdown cells downregulate Mevalonate pathway enzymes

(biosynthesis schematic on right) compared to cells infected with shCTRL hairpins (from

RNAseq FPM values).

(B) Immunoassay for ERK5, HMGCR, and HSP90 expression in control and ERK5 knock-down

NHJ29 hSCLC cells. The molecular weights are indicated on the left (in kDa). The amount of

ERK5 and HMGCR relative to the first control and to HSP90 are indicated below the signal

corresponding to each protein.

(C) Treatment of 8 SCLC cell lines (7 human cell lines and the mouse cell line KP1) with

increasing doses of atorvastatin in reduced (2%) serum media; color corresponds to % cells

alive compared to vehicle-treated controls after 2 or 5 days at each concentration; n=3

independent experiments per treatment.

(D) Concentrations of atorvastatin necessary to kill 50% of NHJ29 cells (IC50) infected with

shCTRLs are higher than concentrations needed to kill cells with ERK5 knockdown; grey and

black dots represent two independent hairpins per group; n=3 experiments, **, p≤0.005 (t-test).




Published OnlineFirst January 22, 2020.Cancer Res Sandra Cristea, Garry L. Coles, Daniel Hornburg, et al. cell lung cancerThe MEK5-ERK5 kinase axis controls lipid metabolism in small

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